STATISTICAL MODELING AND FORECASTING OF ARIMA AND ARIMAX MODELS FOR FOOD GRAINS PRODUCTION AND NET AVAILABILITY OF INDIA

Agricultural development policies in India have aimed at reducing hunger, food insecurity, malnourishment and poverty at a rapid rate. The present work is designed with specific objectives to study the trend analysis of rice, wheat and total food grain in India for the period starting from 1950-2019. For stochastic trend model estimation, time series parametric regression models i.e. Linear model, Quadratic model, Exponential model, Logarithmic model, Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive Integrated Moving Average with explanatory variables (ARIMAX) were analyzed for estimating an appropriate econometric model to capture the trend of major food grain viz. rice, wheat, total food grain production and net availability of the country. Several goodness of fit criteria viz. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and maximum R-squared values was worked out for finding best fitted models. Kolmogorov-Smirnov (K-S) test and Run-test were used to estimate the ‘Normality’ and ‘Independence’ of residuals of all data series respectively. By using the best fitted models, it was observed that the availability of rice (70.05 kg/year), wheat (70.73 kg/year) and total food grain (182.96 kg/year) will decrease in 2021 as comparatively to this year.

Time Series Modeling and Forecasting on Pulses Production Behavior of India

The present investigation was an attempt to study the trend behavior of pulses production, productivity, and net availability of India. For stochastic trend estimation, time series parametric models i.e. Linear, Quadratic, Exponential, Logarithmic, Power, ARIMA, and ARIMAX models were worked out and compared for estimating appropriate econometric model to capture the trending behavior of pulses data series considered in this study. Based on the performance of goodness of fit criterion i.e. R2, RMSE, MAPE, MAE, AIC, and SBC were considered to find the best-fitted model. Cross-correlation function (CCF) was employed to check the relationship with explanatory variables for building ARIMAX model. The assumption of ‘Normality’ of error term was estimated by using K-S- test and S-W- test, whereas Run-test was used to examine the ‘Independency of randomness’ of the error term. From the forecasted figure, pulses production will reach 24.34 MT in 2025-26, whereas productivity will gain 877.36 kg/ha in the same year. Per capita net availability of pulses will reach 24.89 kg/year in 2025-26. This research helps in formulating the national agriculture policymaker.

Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries

This study attempted to examine the future behaviour of monthly average rainfall and temperature of South Asian countries by using the Seasonal Autoregressive Moving Average model. Mann Kendall trend test with Sen’s Slope Estimator, to find the trending behaviour of all data series. The study has also been attempted to compare the above methods with the help of actual data. The monthly average rainfall and temperature of South Asian countries except Afghanistan and Maldives viz. Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka data from January, 1961 to December, 2016 have been collected from World Bank Group, Climate change knowledge portal. For estimating the trending behaviour, a non-parametric model such as the Mann–Kendall test was used with Sen’s slope estimation to determine the magnitude of the trend. Box–Jenkins methodology was also used to develop the model and estimate the forecasting behaviour of rainfall and temperature in South Asian countries. Forecasting is carried out for both monthly rainfall and the average temperature of all the countries using best fitted models based on the data series. The monthly data from January, 1961 to December, 2010 are considered for validation of the model can be regarded as in-sample forecast and the data from January, 2011 to December, 2016 are used as out-sample forecast. The forecasting values with 95% confidence limit from January, 2011 to December, 2021 using best- fitted models for both rainfall and temperature. We conclude that climate change occurs for both rainfall and temperature in South Asian countries from the study period. The selected model can be used for forecasting both rainfall and temperature of respective countries from January, 2011 to December, 2021. As the climatic data analysis is valuable to understand the variation of global climatic change, this study may help for future research work on rainfall and temperature data.

Estimation of fish production in India using ARIMA,Holt’s Linear, BATS and TBATS models

Application of Data Processing and Decision Support System for Identifying Best Management Practices

STATE OF ART OF SARIMA MODEL IN SECOND WAVE ON COVID-19 IN INDIA

The COVID-19 pandemic is wreaking havoc on society and the current situation the country is in now clearly
shows the failure of predicting the second wave in India. Several variants of the coronavirus responsible for COVID-19 have been detected around the world. With several mutations, some are said to be more contagious than the original strain; the recent one is the Indian variant. This necessitates a wholesome study for modeling and forecasting COVID-19 cases in India, which will help us to be prepared. The bottleneck underlines here is the very nature of the virus, which is continuously mutating and unavailability to real-time and precise data for the purpose. This paper is an effort in this regard to the model and forecast the second wave in India. The dataset covers the period 2021-01-16 to 2021-05-16, where different models were used. SARIMA modeling was used for forecasting the cases, deaths and vaccinations using the best-fitted model. The results depict that the prediction was good as the predicted figures were found to be close to the observed values. In this context, we tried to study the pattern of propagation of this variant in India by modeling and forecasting the new deaths, new cases, total deaths, total cases and the total vaccination. Findings are alarming, especially, in the context of the difficulties of the
health system in India.

Constraints Faced by Farmers and Intermediaries to Getting Minimum Support Price (MSP) in Haryana

The present study was conducted in Hisar grain market during 2018-19. For the study primary data was collected from thirty farmers and thirty market intermediaries about constraints faced to get Minimum Support Price. Most of the farmers were found agree with the statement like Online registration of farmers, Purchasing limit of produce, Illiteracy of farmer, Date allotment procedure for purchasing the produce and During peak season/heavy glut in arrival low market prices even below MSP. Most of the market intermediaries were found agree with the statement like Low wage rate fix by government for labour work, Problem of moisture content in produce faced by middleman, Delay in payment and Price fluctuation in agricultural commodities.

Modeling and forecasting meteorological factors using BATS and TBATS models for the Keonjhar district of Orissa

Weather factors such as temperature and humidity are indispensable for good agriculture.The best-suitable products can be selected according to the optimal of these weather factors. In this study, data on maximum temperature, minimum temperature, morning relative humidity and evening relative humidity was analyzed from 31st January, 1921 to 31st December, 2020 in India. The BATS (Exponential Smoothing Method + Box-Cox Transformation + ARMA model for residuals) and TBAT (BATS + Trigonometric Seasonal) models are conducted for forecasting procedures. According to some selection criteria, the best models are specified for all weather factors. Extensive tables and graphics are presented in the study. The data series was divided into train set and test set. The result obtained from train set based on goodness of fit, BATS model performed as a best model. For error estimation using testing data set, BATS model performed well for maximum temperature, morning and evening relative humidity. Both models were performed significantly at par in minimum temperature data series. Using the results and forecasts obtained in this study, the researcher or scientist should be focused on the weather condition which is more concerned parameter for agriculture.

Modeling and Forecasting of Producer Price Index (PPI) of Cheese Manufacturing Industries

Modeling and forecasting of complex time series data has grown as an attractive field thanks to machine learning. The PPI (Producer Price Index) of cheese manufacturing businesses was examined in this study utilizing a machine learning technique. Training and testing data sets were created for the goal of creating and validating a model. After that, we built deep learning models such as LSTM, BILSTM,
and GRU and tested them on a training data set using metrics such as ME, RMSE, MAE, MPE, MAPE, and ACF1. These deep learning models were compared on the basis of RMSE for the testing data set. On this set of data, the LSTM model outperforms the BILSTM and GRU models in terms of machine learning performance. These three models’ forecasting abilities are nearly identical. Policymakers and
academics may find this study useful in building a body of knowledge about PPI in the cheese manufacturing industry. As a result, we feel that this work can be used as a textbook on how to apply machine learning techniques to complex time series.

Modelling and Forecasting of Maize Production in South Asian Countries

The challenges of fighting poverty and enhancing food security in South Asia have made maize a strategic crop in this region. In this study, maize production in South Asia, encompassing Afghanistan, Bangladesh, Bhutan, China, India, Nepal, Pakistan, and Sri Lanka, was analysed and projected from 1961 to 2027 using state-space and ARIMA models. The estimation outcomes demonstrated the state-space models’
superior performance in predicting trends in maize output for all eight time series. Additionally, the forecast estimation revealed that we anticipated an uptick in the output of maize in these nations; this finding would be encouraging for the countries in this region as it would heighten the problem of food security. India would be leading countries in maize with production of 380438 thousand tonnes in 2027.

Modeling and Forecasting of Pulses Production in Madhya Pradesh

Pulses are edible dry seeds of the leguminous family, an essential nutritious element to ensure protein for the vegetarian population of India. There seems to be a gap in their domestic production in one of the leading Indian states, Madhya Pradesh. This work aims to study and forecast the future indicators of production, productivity and cultivation area for three prominent pulse crops of gram, soybean and tur in the state of Madhya Pradesh. The paper uses data for 1980-2019 to study the three indicators. Using time series
techniques (specifically ARIMA model forecasting), the study predicts that productivity will increase for gram crop, area and production will see a growth for soybean crop while all the indicators will have a decreasing trend in case of tur. The results have specific importance for the policymakers to formulate the right reforms to ensure in the future that the demand is met with pulses production to maintain the
nutritional security of the population.

Modeling and Forecasting of Sugarcane Production in South Asian Countries

Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and the sugarcane industry has immense potential to contribute towards its economic development. Hence, the precise and timely forecast of sugarcane production is of concern for farmers, policy makers and other stakeholders. In this manuscript, we strived to forecast the production
and growth rate of this important commodity using standard statistical approaches. The ARIMA (Auto Regressive Integrated Moving Average) and ETS (Exponential Smoothing) models were applied and compared on the basis of their forecasting efficiency for South Asia countries. This
study also investigated the trends in sugarcane production in the region and studies the causes of the decline in production of sugarcane in Sri Lanka and Bangladesh. Furthermore, the expected production for following 7 years was computed using both models. In addition, we also calculated the projected growth rates of sugarcane production of South Asian countries over the years 2020-2027.

Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India

Modelling and forecasting of electricity consumption used inagriculture purpose in India